It’s one of Hilary Mason’s favourite photographs from a slide presentation she uses to explain the idea of “data superpowers.” It shows President Barak Obama on the campaign trail, speaking to an audience filled with people who are holding up not signs, but smartphones and tablets.

“They’re all documenting their experience,” she said, noting that you wouldn’t have seen the same sight less than 10 years ago. “Humanity stays the same but the technology changes pretty quickly . . . Data is increasing and we better figure out what the heck we want to do with it.”

Mason, former chief scientist at URL shortening service Bit.ly, visited Canada earlier this year as part of the key speakers at the CODE Inspiration Day that kicked off Treasury Board Secretariat’s 48-hour Canadian Open Data Experience (CODE) competition. Though she now works as a data scientist in residence at Accel Partners, a venture capital firm based in Palo Alto, Mason is a good example of the kind of specialist that CIOs may need to work with and support as they attempt to make sense of a growing volume of unstructured information.

Though CODE was focused on the use of open data that governments release in increments, Mason suggested it was a good way of learning about how to handle big data, which she defined as “data that’s too big to fit into Microsoft Excel, or that you can’t analyze on one computer anymore.” This, of course, is a moving target, since what a single computer can handle changes almost by the year, if not sooner.

“It does merit a new term, not because we’re doing anything new, but we’re combining multiple skills in one professional,” Mason said. “We’re doing math, thinking of how to symbolically represent something, writing code, taking the model you dreamed up and apply it, and so on.”

Success as a data scientist requires things that aren’t that different than achieving it as a CIO, based on Mason’s remarks. It’s about understanding the priorities of the business and, most importantly, asking the right questions. Data scientists need to feel they are working on something that contributes great value, Mason said.

“There is no greater insult for a data scientist than to suggest you have an elegant solution to an irrelevant problem.”